Hire Mohammed T. - Generative AI Architect

Reduced month-end financial close from 10 days to 4 days (60%) by architecting an enterprise AI decision intelligence platform using Google ADK multi-agent orchestration, RAG-based document grounding, and LLMs across GCP Vertex AI, serving 50K+ enterprise documents with 92% retrieval relevance; deployed LSTM and Transformer-based models for financial forecasting achieving 35% improvement in prediction accuracy with automated retraining, drift detection, and CI/CD model deployment. Eliminated 3 days of manual CFO deck preparation per cycle by building an AI-powered variance narrative system using LangChain, ChromaDB, and GPT-4/Gemini with prompt engineering, auto-decomposing variances into price, volume, mix, and FX drivers; cut manual reconciliation by 40% and variance analysis time by 94% through ML-based anomaly detection (Isolation Forest, Autoencoders) with 95% precision, integrated into Google ADK agent workflows with MCP-based tool orchestration and human-in-the-loop approval gates. Built end-to-end MLOps infrastructure using Docker, Kubernetes, MLflow, and Apache Airflow, automating model training, evaluation, deployment, and monitoring across distributed inference architectures; exposed model serving endpoints as MCP-compatible tools enabling dynamic agent-driven inference and standardised context sharing across all Google ADK agent workflows. Designed ML model governance and AI evaluation framework with LLM evaluation pipelines (LangSmith, RAGAS, DeepEval), model versioning, hallucination mitigation, SHAP-based explainability, and full audit logging for regulatory compliance; integrated MCP server layer to standardise tool access and context propagation across multi-agent pipelines.

Reduced planning cycle time by 40% and improved forecast accuracy by 28% by designing predictive ML models using XGBoost, LightGBM, and ensemble methods for workforce forecasting, demand planning, and budget optimization across 10+ federated data sources. Cut model deployment time from weeks to hours by migrating legacy systems to cloud-native ML infrastructure using PyTorch, Docker, Kubernetes, AWS SageMaker, and Azure ML with MLflow experiment tracking and automated feature engineering pipelines. Achieved sub-200ms inference latency through model optimization, quantization, and distributed training, serving real-time predictions via FastAPI with OAuth 2.0 authentication and role-based access controls. Established MLOps best practices including automated retraining triggers, drift detection (Prometheus/Grafana), CI/CD pipelines, model registry standards, and continuous evaluation frameworks across production ML systems. Led data migration to cloud ML infrastructure, designing ETL pipelines using Apache Spark and Airflow, validating data integrity across 500+ tables with automated reconciliation and GDPR-compliant data governance.

Reduced integration development time by 30% by designing CDS views, OData services, and custom ABAP programs across SD and FI modules, integrating SAP with third-party OTC trading platforms using BAPIs and RFCs. Improved ETL processing speed by 40% by optimizing SQL queries, implementing table partitioning, and building high-performance HANA XSA database artifacts, eliminating batch processing bottlenecks. Reduced inventory holding costs and cut order processing times by 25% by delivering interactive SAC dashboards with predictive analytics, KPI tracking, and drill-down capabilities for data-driven demand planning.